Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XXXIX-B8, 539-544, 2012
http://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XXXIX-B8/539/2012/
doi:10.5194/isprsarchives-XXXIX-B8-539-2012
© Author(s) 2012. This work is distributed
under the Creative Commons Attribution 3.0 License.
 
30 Jul 2012
EXTRACTION OF BENTHIC COVER INFORMATION FROM VIDEO TOWS AND PHOTOGRAPHS USING OBJECT-BASED IMAGE ANALYSIS
M. T. L. Estomata1, A. C. Blanco2, K. Nadaoka3, and E. C. M. Tomoling1 1Environmental Systems Applications of Geomatics Engineering Research Laboratory, Dept. of Geodetic Eng'g, College of Eng'g, University of the Philippines, Diliman, Quezon City, Philippines
2Geodetic Engineering Faculty, College of Eng'g, University of the Philippines, Diliman, QC, Philippines
3Tokyo Institute of Technology, Ookayama, Meguro, Tokyo, Japan
Keywords: Marine, Oceans, Mapping, Bathymetry, Recognition, Object, Camera, Photography Abstract. Mapping benthic cover in deep waters comprises a very small proportion of studies in the field of research. Majority of benthic cover mapping makes use of satellite images and usually, classification is carried out only for shallow waters. To map the seafloor in optically deep waters, underwater videos and photos are needed. Some researchers have applied this method on underwater photos, but made use of different classification methods such as: Neural Networks, and rapid classification via down sampling. In this study, accurate bathymetric data obtained using a multi-beam echo sounder (MBES) was attempted to be used as complementary data with the underwater photographs. Due to the absence of a motion reference unit (MRU), which applies correction to the data gathered by the MBES, accuracy of the said depth data was compromised. Nevertheless, even with the absence of accurate bathymetric data, object-based image analysis (OBIA), which used rule sets based on information such as shape, size, area, relative distance, and spectral information, was still applied. Compared to pixel-based classifications, OBIA was able to classify more specific benthic cover types other than coral and sand, such as rubble and fish. Through the use of rule sets on area, less than or equal to 700 pixels for fish and between 700 to 10,000 pixels for rubble, as well as standard deviation values to distinguish texture, fish and rubble were identified. OBIA produced benthic cover maps that had higher overall accuracy, 93.78±0.85%, as compared to pixel-based methods that had an average accuracy of only 87.30±6.11% (p-value = 0.0001, α = 0.05).
Conference paper (PDF, 1084 KB)


Citation: Estomata, M. T. L., Blanco, A. C., Nadaoka, K., and Tomoling, E. C. M.: EXTRACTION OF BENTHIC COVER INFORMATION FROM VIDEO TOWS AND PHOTOGRAPHS USING OBJECT-BASED IMAGE ANALYSIS, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XXXIX-B8, 539-544, doi:10.5194/isprsarchives-XXXIX-B8-539-2012, 2012.

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